The development of prosthetic devices is a complex technological field. Prosthetic devices must be engineered to be both aesthetically pleasing and functional. Functionality not only requires that the prosthetic device be mechanically capable of accomplishing the required movements, but there must also be a system by which the user of the prosthetic device can “control” the device. User “control” of prosthetic devices remains an area where advances are still necessary.
At first, simple prosthetic devices were controlled by a different, non-impaired part of the body. For example, using shoulder or stomach movements to initiate elbow bending, wrist movements or grasping of an artificial arm. Preferred methods of controlling prosthetic devices are commonly referred to as “EMG” or electromyogram actuated systems. An EMG-actuated system controls the prosthesis by direct synchronous control over or monitoring of the muscles that were originally responsible for the movement, and near simultaneous control over the prosthesis by the user's brain stimulating the same muscle system that formerly controlled the limb. Previously implemented EMG-actuated systems, while progressing rapidly, have met with limited success and suffer from significant drawbacks.
A recently reported EMG-actuated system, J. Wessberg et al., Real-time prediction of hand trajectory by ensembles of cortical neurons in primates, Nature 408: 361-365, 2000, has overcome some drawbacks normally found in these methods. This method began by recording neuronal impulses of primates generated by a specific type of motor skill. Then, the neuronal impulses were averaged and summed by following a population vector approach. The neuronal impulses associated with a specific mechanical action were then estimated.
This method, although a considerable advance, has two significant drawbacks. First, the method requires a large amount of data. Neuronal impulses of the two subjects were recorded for 12 and 24 months respectively for the prediction of two motor tasks. Such a method would require an inordinate amount of “training” time in order for a patient to teach their prosthesis a relatively small number of motor skills. Second, although the method requires a substantial amount of data collection time, the estimation of the ultimate motor task that corresponds to the specific neuronal impulses is statistically not optimal. Unacceptable ambiguity in the corresponding motor task would result and perhaps even an inability to accomplish more detailed motor tasks.
Therefore, there still exists a need for a method of controlling a prosthetic device through monitoring of neuronal impulses associated with motor tasks that can “learn” a reasonable amount of motor skills in a reasonable time and still offer the user of the prosthesis access to more detailed motor skills.